文献类型：期刊文献

英文题名：A Two-Archive Constrained Multi-Objective Optimization Algorithm Based on Two-Stage Weak Cooperation

作者：Tang, Wenliang[1];Jia, Peng[1];Yang, Zehao[1];Guo, Weibin[1];Ding, Weichao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2026

卷号：38

期号：4

外文期刊名：CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

收录：;EI(收录号：20260720066181);WOS:【SCI-EXPANDED(收录号:WOS:001698737100008)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 62403201), the Guangxi Science and Technology Innovation Platform Program ("Leitai" Action Plan - Guangxi Laboratory Capacity Building) (LT2504240026), the Shanghai Pilot Program for Basic Research (22TQ1400100-16), and the Natural Science Foundation of Shanghai (Grant No. 23ZR1414900).

语种：英文

外文关键词：constrained and multi-objective optimization; evolutionary algorithm; two-archive; two-stage; weak-cooperation

摘要：Constrained multi-objective optimization problems widely exist in real-world applications, yet remain challenging due to the coexistence of multiple conflicting objectives and constraints. This study proposes a two-archive constrained multi-objective optimization algorithm with two-stage weak cooperation, named BWC-TAA. First, based on the two-archive framework, a weak cooperation interaction mechanism is designed, in which the CA and DA evolve independently. They only merge offspring during the update stage to select high-quality individuals, thereby preventing solutions from being confined to the parent population range. Second, a two-stage evolutionary strategy is introduced to dynamically adjust the optimization objectives, enabling CA and DA to adopt different strategies in different phases. Finally, BWC-TAA is evaluated on benchmark constrained problems against eight state-of-the-art algorithms. The experimental results demonstrate that BWC-TAA significantly outperforms the compared algorithms in terms of convergence and diversity indicators.